Biology subjects

Wasinger, G.

Publications and source records attributed to Wasinger, G..

4 recordsLinked to original sources

bioRxiv2026

Fibroblast-derived thrombospondin-1 shapes macrophage polarization in advanced human co-culture models

BackgroundTumor-associated macrophages (TAMs) are key drivers of the immunosuppressive tumor microenvironment (TME), supporting tumor progression through diverse functions. However, mechanistic studies of TAM polarization remain limited by the lack of physiologically relevant human model systems that capture stromal-immune interactions and macrophage heterogeneity. MethodsWe established advanced human co-culture systems that integrate healthy donor-derived macrophages with patient-derived organoids and tumoroids (PDOs and PDTs), as well as matched normal fibroblasts (NFs) and cancer-associated fibroblasts (CAFs). These multicellular models enabled the investigation of interactions among stromal, epithelial, and immune cells within tumor and adjacent normal tissue environments. ResultsThe co-culture systems recapitulated distinct macrophage states associated with tumor and adjacent normal environments and identified fibroblasts as major regulators of macrophage phenotypes. CAFs promoted macrophage metabolic remodeling characterized by altered lipid handling and enrichment of TAM-like signatures. Mechanistically, we identified thrombospondin 1 (TSP1) as a CAF-secreted factor linked to metabolic priming. Recombinant TSP1 induced transient lipid accumulation followed by mitochondrial remodeling. In tumor co-culture conditions, CD36 inhibition reduced lipid accumulation in macrophages, supporting a role for TSP1-linked lipid crosstalk in stromal-immune interactions. ConclusionOur study establishes advanced patient-derived co-culture models as a platform to investigate human TAM biology and stromal-immune interactions in CRC. Using these systems, we identify a fibroblast-associated TSP1-lipid axis linked to macrophage metabolic remodeling and TAM-like polarization, highlighting stromal metabolic communication as a potential targetable feature of the CRC microenvironment. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=176 HEIGHT=200 SRC="FIGDIR/small/728363v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@11b8c9borg.highwire.dtl.DTLVardef@1ac3aa2org.highwire.dtl.DTLVardef@3da223org.highwire.dtl.DTLVardef@5fc3a7_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

Biobank of genetically defined murine prostate cancer tumoroids uncovers oncogenic pathways and drug vulnerabilities driven by PTEN-loss

Prostate cancer (PCa) is the second most common cancer in men and shows high inter- and intra-patient heterogeneity. Consequently, treatment options are limited and there is a lack of representative preclinical models. Here we establish a comprehensive biobank of murine organoids and tumoroids that reflect common patient mutations. We demonstrate that the deletion of Pten alone, or in combination with Stat3, or Tp53, drives the activation of cancer-related pathways in both prostate organoids and tumour-derived tumoroids. A medium-throughput drug screen identified two potent compounds, the PDPK1/AKT/FLT dual pathway inhibitor and the sirtuin inhibitor tenovin-6, which effectively suppressed tumoroid proliferation. Notably, these compounds also inhibited the growth of several human PCa cell lines and displayed synergistic effects when combined with the standard-of-care antiandrogen enzalutamide. Together, our findings provide evidence that murine tumoroids are versatile preclinical models for studying PCa tumorigenesis and drug sensitivities to develop novel therapeutic options for PCa patients.

cancer biology↗

bioRxiv2025

Thyroid Hormone Receptor Beta Signaling is a Targetable Driver of Prostate Cancer Growth

Thyroid hormone (TH) signaling plays a major role in the development, energy homeostasis, and metabolism of most tissues. Recent observations have identified THs as drivers of prostate cancer (PCa) tumor development and progression. We reported that the T3-scavenger protein {micro}-crystallin (CRYM) regulates the development and progression of PCa and that this involved crosstalk with the androgen receptor (AR) signaling. However, the mechanisms remain incompletely understood. Here, we explored the role of thyroid hormone receptor {beta} (TR{beta}), which is the main effector of TH signaling, in the context of PCa. The use of the TR{beta}-selective antagonist NH-3 inhibited PCa cell proliferation in vitro and reduced tumor size in PCa xenograft models. Notably, NH-3 was highly effective in the engrafted 22Rv1 cell line, a model for castration-resistant PCa (CRPC). Mechanistic studies revealed that NH-3 downregulates AR and the AR target genes Nkx3.1 and KLK3 (PSA). NH-3 was a more effective anticancer agent than enzalutamide and showed synergistic properties in combined use. Evidence from human datasets corroborates our findings whereby elevated TR{beta} expression and mutations in TH signaling pathways are associated with the onset of PCa. Collectively, these results establish TR{beta} as a mediator of tumorigenesis in PCa and identify NH-3 as a promising therapeutic agent for targeting AR signaling, particularly in CRPC.

cancer biology↗

bioRxiv2023

Integrating weighted correlation network analysis and machine learning identifies common trajectories of prostate cancer

BackgroundProstate cancer diagnosis and prognosis is currently limited by the availability of sensitive and specific biomarkers. There is an urgent need to develop molecular biomarkers that allow for the distinction of indolent from aggressive disease, the sensitive detection of heterogeneous tumors, or the evaluation of micro-metastases. The availability of multi-omics datasets in publicly accessible databases provides a valuable foundation to develop computational workflows for the identification of suitable biomarkers for clinical management of cancer patients. ResultsWe combined transcriptomic data of primary localized and advanced prostate cancer from two cancer databases. Transcriptomic analysis of metastatic tumors unveiled a distinct overexpression pattern of genes encoding cell surface proteins intricately associated with cell-matrix components and chemokine signaling pathways. Utilizing an integrated approach combining machine learning and weighted gene correlation network modules, we identified the EZH2-TROAP axis as the main trajectory from initial tumor development to lethal metastatic disease. In addition, we identified and independently validated 58 promising biomarkers that were specifically upregulated in primary localized or metastatic disease. Among those biomarkers, 22 were highly significant for predicting biochemical recurrence. Notably, we confirmed TPX2 upregulation at the protein level in an independent cohort of primary prostate cancer and matched lymph node metastases. ConclusionsThis study demonstrates the effectiveness of using advanced bioinformatics approaches to identify the biological factors that drive prostate cancer progression. Furthermore, the targets identified show promise as prognostic biomarkers in clinical settings. Thus, integrative bioinformatics methods provide both deeper understanding of disease dynamics and open the doors for future personalized interventions.

bioinformatics↗